Appendix 3-A: Description of Scenario Inputs Used in SAM

The SAM model described in Section 2 of the SAP document Development of a Spatial Aquatic 

Model (SAM) for Pesticide Assessments uses scenarios that are created by intersecting soil 

map units from USDA’s Soil Survey Geographic (SSURGO) database, general land cover/crop 

groups of the Cropland Data Layer, and weather station grids generated from NOAA data. The 

primary sources for model inputs are described in Section 3.1. These represent what USEPA 

OPP has determined to be the best nationally-available data for model inputs for SAM. The data 

and model frameworks are designed so that, as newer and/or improved datasets become 

available, these can be used in place of existing datasets.

The table below summarizes the input data extracted from these sources and compiled for 

each soil /land cover/weather station combination.

Table 1 – Scenario inputs used in SAM.

ParameterParameter 

Name / 

Description

Units Source Extraction/ 

Derivation

Major Factor in 

Model 

Sensitivity?

Relationships/ 

correlation

Soil factors affecting runoff and pesticide movement from fields into water

hydgrp Hydrologic soil 

group (HSG), 

used to 

determine 

curve number

A, B, C, D SSURGO for Soil 

Map Unit: 

HYDGRPDCD 

(dominant HSG) 

from muaggatt

Taken directly 

from muaggatt 

table. A/D, B/D, 

and C/D HSG 

groups were 

assigned HSG D 

for curve number 

determination. 

Major: Used in 

combination 

with land cover 

to determine 

curve number

Determined by 

hydrologic 

conductivity, 

depth to drainage 

restrictive layers, 

which are 

influenced by soil 

texture, structure, 

organic matter 

content, bulk 

density, 

mineralogy

cn_ag

cn_ 

fallow

Runoff curve 

numbers of 

antecedent 

moisture 

condition for 

fallow, 

cropping, and 

residue.

Number 

between 

1 and 

100

Hydrologic soil 

group from 

SSURGO, land 

use from land 

cover data 

groups & crop 

use. Curve 

number 

guidance from 

USDA NRCS 

(2008b). 

Using HSG, 

general land 

cover groups, 

follow curve 

number guidance 

in Appendix 7.

Major: 

Determines the 

portion of 

rainfall that 

runs off the 

land, 

potentially 

carrying 

pesticides with 

it. 

Determined by 

hydrologic soil 

group, land cover 

type, and

management 

practices
Parameter

Parameter 

Name / 

Description

Units Source Extraction/ 

Derivation

Major Factor in 

Model 

Sensitivity?

Relationships/ 

correlation

organic Percent soil 

organic 

carbon (OC)

for the surface 

layer

% SSURGO 

chorizon table 

(linked to major 

component and 

map unit): 

OM_R 

(chorizon) for 

top horizon 

(hzdept_r=0)

Convert from 

percent organic 

matter (om_r) to 

organic carbon 

(OC) using the 

equation:

OC=(om_r)/1.724

Major: In 

combination 

with the 

pesticide 

sorption 

coefficient, 

determines 

how much 

pesticide is held 

(sorbed) to soil 

and how much 

is dissolved in 

water (available 

for runoff) 

Impacts (directly 

or indirectly) 

density, water 

holding 

properties, 

erodibility; in 

combination with 

chemical Koc, 

impacts the 

amount of 

pesticide held on 

soil

bd1

bd2

Bulk density

for the surface

(1) and 

subsurface (2)

horizons

g/cm3 SSURGO 

chorizon table 

(linked to major 

component,

map unit): 

DBTHIRDBAR_R 

(chorizon) for 

each horizon, 

along with 

horizon depths 

(hzdept_r)

bd1 = 

dbthirdbar_r for 

hzdept_r=0 

(surface horizon)

bd2 = sum of 

(dbthirdbar_r for 

all horizons below 

10 cm x thickness 

of the horizon) 

divided by the 

total thickness

Secondary Related to soil 

texture (sand, silt, 

clay), structure, 

organic content. 

Influences soil 

permeability/ 

drainage, which 

indirectly 

influences 

hydrologic soil 

groups

fc1

fc2

Field capacity

for the surface

(1) and 

subsurface (2)

horizons, 

roughly 

reflective of 

the amount of 

water retained 

after large 

pores have 

drained 

(represented 

by the 1/3 bar 

water content)

cm3/cm3 SSURGO 

chorizon table 

(linked to major 

component,

map unit): 

WTHIRDBAR_R 

(chorizon) for 

each horizon, 

along with 

horizon depths 

(hzdept_r)

fc1 = wthirdbar_r 

for hzdept_r=0 

(surface horizon)

fc2 = sum of 

(wthirdbar_r for 

all horizons below 

10 cm x thickness 

of the horizon) 

divided by the 

total thickness

Secondary

Combined with 

wilting point, 

influences 

water holding 

capacity of soil, 

which 

influences plant 

growth, 

irrigation 

needs, how 

much water soil 

can hold before 

runoff.

Related to soil 

texture (sand, silt, 

clay), structure, 

bulk density, 

organic content. 
Parameter

Parameter 

Name / 

Description

Units Source Extraction/ 

Derivation

Major Factor in 

Model 

Sensitivity?

Relationships/ 

correlation

wp1

wp2

Wilting point

for the surface

(1) and 

subsurface (2)

horizons, 

roughly 

reflective of 

the minimum 

water content 

in which plants 

cannot draw 

water from 

the soil

(represented 

by the 15 bar 

water content)

cm3/cm3 SSURGO 

chorizon table 

(linked to major 

component,

map unit): 

WFIFTEENBAR_

R (chorizon) for 

each horizon, 

along with 

horizon depths 

(hzdept_r)

wp1 = 

wfifteenbar_r for 

hzdept_r=0 

(surface horizon)

wp2 = sum of 

(wfifteenbar_r for 

all horizons below 

10 cm x thickness 

of the horizon) 

divided by the 

total thickness

Secondary

Combined with 

field capacity, 

influences 

water holding 

capacity of soil, 

which 

influences plant 

growth, 

irrigation 

needs, how 

much water soil 

can hold before 

runoff.

Similar to field 

capacity, although 

soil texture (sand, 

silt, clay) and 

organic content 

has a greater 

influence than 

structure on 

wilting point. 

Factors affecting soil erosion and transport of pesticides sorbed to soil particles into water

kwfact Universal soil 

loss equation 

(K) of soil 

erodibility 

factor

Dimensionless 

fraction

SSURGO Soil 

Map Unit, 

joined to 

chorizon table 

through 

muaggatt, 

component 

tables: KWFACT 

for topmost 

horizon 

(hzdept_r = 0)

Extract kwfact (K 

factor, whole soil) 

for surface 

horizon of major 

component in 

chorizon table

Varies, 

depending on 

the sorption 

coefficient of 

the pesticide; 

greater impact 

expected with 

pesticides with 

high sorption 

coefficient.

Degree of 

erodibility 

depends on soil 

texture (silt-sized 

particles are most 

prone), organic 

matter content, 

soil structure 

(how well soil 

particles 

aggregate), 

permeability 

(which is related 

to bulk density 

and soil structure) 
Parameter

Parameter 

Name / 

Description

Units Source Extraction/ 

Derivation

Major Factor in 

Model 

Sensitivity?

Relationships/ 

correlation

slope Land slope, 

the average 

slope gradient 

for the soil 

mapping unit. 

% SSURGO for Soil 

Map Unit: 

SLOPEGRADD 

(wtd. avg. 

slope) from 

muaggatt

Weighted average 

% slope for the 

soil map unit 

(slopegradd) from 

SSURGO 

muaggatt table

Secondary

Used to 

determine the 

USLE LS 

(slope/length) 

factor, which, in 

combination 

with other 

RUSLE 

parameters, 

estimates the 

amount of 

sediment 

moving from 

the field, along 

with any 

pesticide mass 

retained on 

soil. 

Impacts soil 

erosion potential 

in combination 

with the slope 

length. The 

combined impact 

is characterized in 

the USLE LS factor 

below. 

slp_

usle

Slope length

used to 

calculate the 

USLE LS factor. 

Distance from 

point of origin 

of overland 

flow to the 

point where 

gradient 

decreases and 

deposition 

begins

feet SSURGO 

component 

table: 

SLOPELENUS_R 

(linked to 

mapunit)

SLOPELENUS_R in 

component table

Secondary

Used to 

determine the 

USLE LS 

(slope/length) 

factor, which, in 

combination 

with other 

RUSLE 

parameters, 

estimates the 

amount of 

sediment 

moving from 

the field, along 

with any 

pesticide mass 

retained on 

soil. 

Some correlation 

with slope 

gradient, but 

weak. Impacts soil 

erosion potential 

in combination 

with the slope 

length. The 

combined impact 

is characterized in 

the USLE LS factor 

below.
Parameter

Parameter 

Name / 

Description

Units Source Extraction/ 

Derivation

Major Factor in 

Model 

Sensitivity?

Relationships/ 

correlation

uslels Universal soil 

loss equation 

(LS) lengthslope 

topographic 

factor

Dimensionless 

fraction

Table 5.5 in 

PRZM manual 

(Carousel et al, 

2005) provides 

value based on 

% slope 

[SLOPEGRADD 

from SSURGO], 

slope length 

[SLOPELENUS_R 

from SSURGO]

Derive from 

SLOPEGRADD 

from SSURGO 

muaggatt table; 

length from 

flowpath (upslope 

and downslope) 

lengths for the 

basic map units or 

from 

SLOPELENUS_R in 

component table. 

Cross-reference

with Table 5.5 of 

PRZM Manual 

(Carousel et al, 

2005)

Uncertain. 

Sensitivity 

analysis on soil 

erosion 

(USLE/RUSLE) 

model inputs is 

needed. 

Expected to 

have greater 

impact on highKoc chemicals, 

but the extent 

has not been 

tested. 

Derived from 

slope gradient, 

slope length 

parameters 

above. 

cfact_1

cfact_3

Universal soil 

loss cover 

management 

factors for 

fallow, 

cropping, and 

residue (C 

value)

Dimensionless 

fraction

Table 5.7 of the 

PRZM manual

(Carousel et al, 

2005), based on 

crop, 

management, 

rotation 

practices; Ag 

Handbook 703; 

or RUSLE data 

tables. Other 

sources include 

existing PRZM 

scenarios 

Link crop-related 

data to major 

crops in CDL 

general land 

cover classes

Uncertain. 

Sensitivity 

analysis on soil 

erosion 

(USLE/RUSLE) 

model inputs is 

needed. 

Expected to 

have greater 

impact on highKoc chemicals, 

but the extent 

has not been 

tested.

Unknown; related 

to crop type, 

cultivation 

practice.

Manni

ngsN

Manning's N, 

roughness 

coefficient for 

field. 

Dimensionless 

fraction

Use default of 

0.17 for typical 

row crop 

tillage; Table 

5.46 in PRZM 

manual

(Carousel et al, 

2005) provides 

values for other 

conditions.

Link crop-related 

data to major 

crops in CDL 

general land 

cover classes

Uncertain, 

expected small. 

Sensitivity 

analysis on soil 

erosion 

(USLE/RUSLE) 

model inputs is 

needed. 

Expected to 

have greater 

impact on highKoc chemicals, 

but the extent 

has not been 

tested.

Unknown; related 

to crop type, 

cultivation 

practice, but 

varies little. 
Parameter

Parameter 

Name / 

Description

Units Source Extraction/ 

Derivation

Major Factor in 

Model 

Sensitivity?

Relationships/ 

correlation

Crop/Plant Factors that will likely have a moderating influence on pesticide movement offsite

plant Julian day of 

crop planting

day 

number

Derived from

USDA Usual 

Planting and 

Harvesting 

Dates (2006, 

2007, 2010), 

linked to USDA 

Plant Hardiness 

Zone map (see 

Appendix 4a). 

Convert 

day/month to 

Julian day. Link 

crop-related data 

to major crops in 

cdlclass; adjust by 

plant hardiness 

zone. Use 1 for 

noncultivated 

covers.

Expected Major

as it dictates 

the timing of 

pesticide 

application. 

Impact may be 

lessened since 

we are using an 

application 

window rather 

than single 

date. 

harvest Julian day of 

crop harvest

month 

number

Derived from

USDA Usual 

Planting and 

Harvesting 

Dates (2010), 

linked to USDA 

Plant Hardiness 

Zone map (see 

Appendix 4a).

Convert 

day/month to 

Julian day. Link 

crop-related data 

to major crops in 

cdlclass; adjust by 

plant hardiness 

zone. Use 365 for 

noncultivated 

covers.

Uncertain, but 

expected small

in relation to 

planting date.

Dictated by 

planting date, 

time to maturity 

for plant

cintcp Maximum 

rainfall 

interception

storage of 

crop (cm)

cm Table 5.4 in 

PRZM manual 

(Carousel et al, 

2005) provides 

intercept 

storage for 

major crops

(see also 

Appendix 4a).

Link crop-related 

data to major 

crops in CDL 

general land 

cover classes

Uncertain, 

expected small. 

Has not been 

evaluated, but 

expected small 

in relation to 

curve number 

impacts.

Related to crop 

type.

covmax Maximum 

areal coverage 

of canopy (%)

% Based on 

existing PRZM 

scenarios (see 

Appendix 4a).

Link crop-related 

data to major 

crops in CDL 

general land 

cover classes

Uncertain, 

expected small. 

Has not been 

evaluated, but 

expected small 

in relation to 

curve number 

impacts.

Related to crop 

type. For most 

crops, 80-100% 

maximum.
Parameter

Parameter 

Name / 

Description

Units Source Extraction/ 

Derivation

Major Factor in 

Model 

Sensitivity?

Relationships/ 

correlation

amxdr Maximum 

active rooting 

depth of crop 

(cm)

cm Table 5.9 in 

PRZM manual 

(Carousel et al, 

2005) provides 

average rooting 

depth for major 

crops (columns 

are off). Other 

sources (USDA) 

are available for 

regional 

variations (see 

Appendix 4a).

Link crop-related 

data to major 

crops in cdlclass. 

Adjust amxdr so 

that it does not 

exceed the total 

soil depth.

Varies, impact 

is on activating 

irrigation. 

Minimal except 

where irrigation 

occurs. 

Related to crop, 

soil depth.

Weather Factors beyond rainfall

sfac Snowmelt 

factor (cm/C), 

used to 

calculate 

snowmelt 

rates in 

relation to 

temperature.

cm/C Table 5.1 in 

PRZM manual 

(Carousel et al, 

2005) gives 

range in SFAC 

related to 

forest covers.

Maximum value 

of the minimum 

range of values 

for cover type 

reported in Table 

5.1 (0.36 for 

crops, nonforested land 

covers; 0.16 for 

forest covers)

Uncertain, 

expected 

minimal. Has 

not been 

evaluated. 

Currently 2 

default values 

based on land 

cover, weather.

pfac Pan factor

(dimensionless

), was used to 

convert daily 

pan 

evaporation to 

daily potential 

ET. Current 

weather input

files define 

daily potential 

ET directly, so 

no conversion 

is necessary.

Dimensionless 

fraction

Figure 5.9 in 

PRZM manual 

(Carousel et al, 

2005). Spatial 

pfac layer 

(Farnsworth 

and Peck, 

1982).

Digitized pfac 

layer. Spatial join 

with weather 

station grids (see

Appendix 3b).

Uncertain, 

expected 

minimal. Has 

not been 

evaluated.

Related to 

weather.

rainfall Rainfall 

distribution 

region used to 

calculation 

time of 

concentration 

of peak flow

Figure 5.8 in 

PRZM manual 

(Carousel et al, 

2005).

IREG = 3, based 

on location in 

Type II from 

rainfall 

distribution map 

(Fig 5.8 in PRZM 

manual)

Uncertain, 

expected 

minimal. Broad 

regions across 

country. 
Parameter

Parameter 

Name / 

Description

Units Source Extraction/ 

Derivation

Major Factor in 

Model 

Sensitivity?

Relationships/ 

correlation

anetd Min. depth 

from which 

evaporation is 

extracted

during fallow 

period (cm); 

10 cm = soil 

with limited 

drainage

cm 10 cm for soil 

w/ limited 

drainage. Figure 

5.2 in PRZM 

manual 

(Carousel et al, 

2005) for freedrainage soils.

Digitized Figure 

5.2, using 

midpoint of range 

of values. Default 

of 17.5 used for 

hydro-region 5. 

Uncertain, 

expected 

minimal. Has 

not been 

evaluated.

Used single 

default value